2,991 research outputs found
Becoming the Expert - Interactive Multi-Class Machine Teaching
Compared to machines, humans are extremely good at classifying images into
categories, especially when they possess prior knowledge of the categories at
hand. If this prior information is not available, supervision in the form of
teaching images is required. To learn categories more quickly, people should
see important and representative images first, followed by less important
images later - or not at all. However, image-importance is individual-specific,
i.e. a teaching image is important to a student if it changes their overall
ability to discriminate between classes. Further, students keep learning, so
while image-importance depends on their current knowledge, it also varies with
time.
In this work we propose an Interactive Machine Teaching algorithm that
enables a computer to teach challenging visual concepts to a human. Our
adaptive algorithm chooses, online, which labeled images from a teaching set
should be shown to the student as they learn. We show that a teaching strategy
that probabilistically models the student's ability and progress, based on
their correct and incorrect answers, produces better 'experts'. We present
results using real human participants across several varied and challenging
real-world datasets.Comment: CVPR 201
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Symbiotic deep learning for medical image analysis with applications in real-time diagnosis for fetal ultrasound screening
The last hundred years have seen a monumental rise in the power and capability of machines to
perform intelligent tasks in the stead of previously human operators. This rise is not expected
to slow down any time soon and what this means for society and humanity as a whole remains
to be seen. The overwhelming notion is that with the right goals in mind, the growing influence
of machines on our every day tasks will enable humanity to give more attention to the truly
groundbreaking challenges that we all face together. This will usher in a new age of human
machine collaboration in which humans and machines may work side by side to achieve greater
heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of
intelligent systems come to the fore in complex systems where the interaction between humans
and machines can be made seamless, and it is this goal of symbiosis between human and machine
that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven
methods have come to the fore and now represent the state-of-the-art in many different
fields. Alongside the shift from rule-based towards data-driven methods we have also seen a
shift in how humans interact with these technologies. Human computer interaction is changing
in response to data-driven methods and new techniques must be developed to enable the same
symbiosis between man and machine for data-driven methods as for previous formula-driven
technology.
We address five key challenges which need to be overcome for data-driven human-in-the-loop
computing to reach maturity. These are (1) the ’Categorisation Challenge’ where we examine
existing work and form a taxonomy of the different methods being utilised for data-driven
human-in-the-loop computing; (2) the ’Confidence Challenge’, where data-driven methods must
communicate interpretable beliefs in how confident their predictions are; (3) the ’Complexity
Challenge’ where the aim of reasoned communication becomes increasingly important as the
complexity of tasks and methods to solve also increases; (4) the ’Classification Challenge’ in
which we look at how complex methods can be separated in order to provide greater reasoning
in complex classification tasks; and finally (5) the ’Curation Challenge’ where we challenge the
assumptions around bottleneck creation for the development of supervised learning methods.Open Acces
Leveraging Explanations in Interactive Machine Learning: An Overview
Explanations have gained an increasing level of interest in the AI and
Machine Learning (ML) communities in order to improve model transparency and
allow users to form a mental model of a trained ML model. However, explanations
can go beyond this one way communication as a mechanism to elicit user control,
because once users understand, they can then provide feedback. The goal of this
paper is to present an overview of research where explanations are combined
with interactive capabilities as a mean to learn new models from scratch and to
edit and debug existing ones. To this end, we draw a conceptual map of the
state-of-the-art, grouping relevant approaches based on their intended purpose
and on how they structure the interaction, highlighting similarities and
differences between them. We also discuss open research issues and outline
possible directions forward, with the hope of spurring further research on this
blooming research topic
Machine Learning in Automated Text Categorization
The automated categorization (or classification) of texts into predefined
categories has witnessed a booming interest in the last ten years, due to the
increased availability of documents in digital form and the ensuing need to
organize them. In the research community the dominant approach to this problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
Analysis domain model for shared virtual environments
The field of shared virtual environments, which also
encompasses online games and social 3D environments, has a
system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Large language models (LLMs) have been recently leveraged as training data
generators for various natural language processing (NLP) tasks. While previous
research has explored different approaches to training models using generated
data, they generally rely on simple class-conditional prompts, which may limit
the diversity of the generated data and inherit systematic biases of LLM. Thus,
we investigate training data generation with diversely attributed prompts
(e.g., specifying attributes like length and style), which have the potential
to yield diverse and attributed generated data. Our investigation focuses on
datasets with high cardinality and diverse domains, wherein we demonstrate that
attributed prompts outperform simple class-conditional prompts in terms of the
resulting model's performance. Additionally, we present a comprehensive
empirical study on data generation encompassing vital aspects like bias,
diversity, and efficiency, and highlight three key observations: firstly,
synthetic datasets generated by simple prompts exhibit significant biases, such
as regional bias; secondly, attribute diversity plays a pivotal role in
enhancing model performance; lastly, attributed prompts achieve the performance
of simple class-conditional prompts while utilizing only 5\% of the querying
cost of ChatGPT associated with the latter. We release the generated dataset
and used prompts to facilitate future research. The data and code will be
available on \url{https://github.com/yueyu1030/AttrPrompt}.Comment: Work in progress. A shorter version is accepted to the ICML DMLR
worksho
Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short Term Memory and Autoencoder
Currently, the wide spreading of real-time applications such as VoIP and
videos-based applications require more data rates and reduced latency to ensure
better quality of service (QoS). A well-designed traffic classification
mechanism plays a major role for good QoS provision and network security
verification. Port-based approaches and deep packet inspections (DPI)
techniques have been used to classify and analyze network traffic flows.
However, none of these methods can cope with the rapid growth of network
traffic due to the increasing number of Internet users and the growth of real
time applications. As a result, these methods lead to network congestion,
resulting in packet loss, delay and inadequate QoS delivery. Recently, a deep
learning approach has been explored to address the time-consumption and
impracticality gaps of the above methods and maintain existing and future
traffics of real-time applications. The aim of this research is then to design
a dynamic traffic classifier that can detect elephant flows to prevent network
congestion. Thus, we are motivated to provide efficient bandwidth and fast
transmision requirements to many Internet users using SDN capability and the
potential of Deep Learning. Specifically, DNN, CNN, LSTM and Deep autoencoder
are used to build elephant detection models that achieve an average accuracy of
99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the
promising algorithms that does not require human class labeler. It achieves an
accuracy of 97.95% with a loss of 0.13 . Since the loss value is closer to
zero, the performance of the model is good. Therefore, the study has a great
importance to Internet service providers, Internet subscribers, as well as for
future researchers in this area.Comment: 27 page
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